Regions Of Interest Pairs Research Articles

Recovery of resting brain connectivity ensuing mild traumatic brain injury.

Brains reveal amplified plasticity as they recover from an injury. We aimed to define time dependent plasticity changes in patients recovering from mild traumatic brain injury (mTBI). Twenty-five subjects with mild head injury were longitudinally evaluated within 36 h, 3 and 6 months using resting state functional connectivity (RSFC). Region of interest (ROI) based connectivity differences over time within the patient group and in comparison with a healthy control group were analyzed at p < 0.005. We found 33 distinct ROI pairs that revealed significant changes in their connectivity strength with time. Within 3 months, the majority of the ROI pairs had decreased connectivity in mTBI population, which increased and became comparable to healthy controls at 6 months. Within this diffuse decreased connectivity in the first 3 months, there were also few regions with increased connections. This hyper connectivity involved the salience network and default mode network within 36 h, and lingual, inferior frontal and fronto-parietal networks at 3 months. Our findings in a fairly homogenous group of patients with mTBI evaluated during the 6 month window of recovery defines time varying brain connectivity changes as the brain recovers from an injury. A majority of these changes were seen in the frontal and parietal lobes between 3 and 6 months after injury. Hyper connectivity of several networks supported normal recovery in the first 6 months and it remains to be seen in future studies whether this can predict an early and efficient recovery of brain function.

Altered Functional Connectivity Strength in Abstinent Chronic Cocaine Smokers Compared to Healthy Controls.

Past research involving cocaine and resting-state functional connectivity (RSFC) has shown altered functional connectivity within the frontal and between the frontal and other cortical and subcortical brain regions in chronic users of cocaine. However, there have been discrepancies in literature regarding the relationship between RSFC between brain regions and cocaine use behavior. This study explored the RSFC between brain regions in cocaine smokers abstinent from cocaine use for 72 h and healthy controls. Also, the relationship between RSFC between brain regions and various cocaine use measures (cocaine use duration; frequency, and money spent on cocaine/week) was examined. Twenty chronic cocaine users and 17 controls completed a resting-state scan and an anatomical MPRAGE scan. Group independent component analysis performed on functional magnetic resonance imaging data identified 13 ICs pertaining to distinct resting-state networks, and group-level differences were examined. To examine inter-network functional connectivity between brain regions, these 13 ICs were divided into 61 distinct regions of interest (ROIs). Correlations were calculated between 61 ROI time series. For the ROI pairs that significantly differed from controls in connectivity strength, correlations were computed between connectivity strength and cocaine use measures. Results showed an enhanced RSFC within the sensory motor cortex and the left frontal-parietal network in cocaine users than controls. An increased inter-network RSFC between frontal-temporal and frontal-parietal brain regions, and a decreased RSFC between parietal-parietal, occipital-limbic, occipital-occipital, and occipital-parietal brain regions was found in cocaine users. This study demonstrated that intra-network connectivity strength of sensory motor cortex was negatively correlated with years of cocaine use. Inter-network connectivity strength between occipital-limbic brain regions was positively correlated with years of cocaine use, while connectivity strength within occipital brain regions was negatively related to cocaine use frequency and money spent on cocaine per week in abstinent cocaine users.

Anthropometric profile and diabetic foot risk: a cross-sectional study using thermography.

Diabetes is one of the greatest todays public health problems with enormous social and economic implications for society. Diabetic foot disorders represent a substantial economic burden with detrimental effects on quality of life with special impairment in physical domain. Early detection strategies of these complications should be implemented in order to avoid possible wounds, ulcerations and amputations. This work can be characterized as a cross-sectional study with an analytical approach. It involved 44 volunteers of both genders (22 women and 22 men; 66.70 ± 6.26 years of age) with type 2 diabetes (diagnosed at 11.84 ± 8.22 years), selected among the candidates to Diabetes em Movimento® (a community-based exercise program for patients with type 2 diabetes developed in the city of Vila Real, Portugal). Foot plantar thermal images were acquired through a high-resolution infrared camera (FLIR Systems Inc. Model SC2000; 320 × 240 pixels). Three regions of interest (ROI) were defined for evaluation: first finger, fifth finger and the heel. From the three pairs of ROIs evaluated, the higher temperature asymmetry was selected for diabetic foot risk analysis. The results showed the existence of a positive and significant association between BMI and fat mass with asymmetries in feet temperature. Three subjects with diabetes-related foot complications (ROIs higher temperature asymmetry ≥ 2.20 °C) were identified in the sample. All participants with detected diabetes-related foot complications were obese (BMI ≥ 30 kg/m2) with high levels of body fat (≥ 45%). It can be concluded that exist a positive association either of BMI (r=0.399, p=0.007) either of body fat percentage (r=0.432, p=0.003), with diabetic foot risk in patients with type 2 diabetes.

Multimodal Imaging of Brain Connectivity Using the MIBCA Toolbox: Preliminary Application to Alzheimer’s Disease

The Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox is a fully automated all-in-one connectivity analysis toolbox that offers both pre-processing, connectivity, and graph theory analysis of multimodal images such as anatomical, diffusion, and functional MRI, and PET. In this work, the MIBCA functionalities were used to study Alzheimer's Disease (AD) in a multimodal MR/PET approach. Materials and Methods: Data from 12 healthy controls, and 36 patients with EMCI, LMCI and AD (12 patients for each group) were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu), including T1-weighted (T1-w), Diffusion Tensor Imaging (DTI) data, and 18F-AV-45 (florbetapir) dynamic PET data from 40-60 min post injection (4x5 min). Both MR and PET data were automatically pre-processed for all subjects using MIBCA. T1-w data was parcellated into cortical and subcortical regions-of-interest (ROIs), and the corresponding thicknesses and volumes were calculated. DTI data was used to compute structural connectivity matrices based on fibers connecting pairs of ROIs. Lastly, dynamic PET images were summed, and the relative Standard Uptake Values calculated for each ROI. Results: An overall higher uptake of 18F-AV-45, consistent with an increased deposition of beta-amyloid, was observed for the AD group. Additionally, patients showed significant cortical atrophy (thickness and volume) especially in the entorhinal cortex and temporal areas, and a significant increase in Mean Diffusivity (MD) in the hippocampus, amygdala and temporal areas. Furthermore, patients showed a reduction of fiber connectivity with the progression of the disease, especially for intra-hemispherical connections. Conclusion: This work shows the potential of the MIBCA toolbox for the study of AD, as findings were shown to be in agreement with the literature. Here, only structural changes and beta-amyloid accumulation were considered. Yet, MIBCA is further able to process fMRI and different radiotracers, thus leading to integration of functional information, and supporting the research for new multimodal biomarkers for AD and other neurodegenerative diseases.

Incorporation of fuzzy spatial relation in temporal mammogram registration

This paper presents a method for incorporating fuzzy sets based spatial relations in registering temporal mammogram pairs. In the proposed method, four spatial relations, to the right of, to the left of, below, and above, are considered. The histogram of all possible angles between all pairs of points in a pair of regions of interest (ROIs) is treated as a fuzzy set and spatial relations between the pair of ROIs are characterized by measuring to what degree this fuzzy set approaches the four spatial relations. Based on the spatial relations, association of ROIs of temporal mammogram pairs is then treated as a graph matching problem and registration of temporal mammogram pairs is realized by finding the common subgraph between two graphs representing a pair of temporal mammograms. 95 pairs of real temporal mammograms are used to test the proposed method. 70.8% of matched ROI pairs are visually identified as “good” matches. When the registration results are incorporated in a cancer detection scheme, the Az score (area under the ROC curve) is improved from 0.846 to 0.852. The results demonstrated that registration of temporal mammograms based on fuzzy spatial relations improves the overall detection efficiency.

Emdr Therapy Changes the Resting-state Eeg

Introduction During the Eye Movement Desensitization and Reprocessing (EMDR) changes of brain electrical activity was recently demonstrated in victims of psychological traumas. Objectives Thirty-one victims of psychological traumas were investigated at the first EMDR session (t0) and at the last one performed after processing the index trauma (t1). Aims To investigate differences in EEG source activity and EEG source functional connectivity (EEG-SFC) in eyes closed condition before the beginning of t0 and t1 EMDR therapy session. Methods Electrical source activity was computed by eLORETA from a 37-channel EEG. EEG-SFC analysis was based on the lagged phase synchronization (LPS), derived by a two-step eLORETA procedure: dimensionality reduction of inverse matrix from 6239 voxels to 28 regions of interest (ROIs); LPS indices computation, for each spectrum band, in all possible ROI pairs. Results Resting-state EEG source activity resulted in a low frequency increase of posterior cingulate cortex and a high frequency (beta2 and gamma) decrease in right prefrontal and parietal cortex between t0 and t1. Significant enhancements of EEG-SFC were detected in t1 respect to t0 between ROI pairs of theta band right temporo-parahippocampal regions and alpha band fronto-parietal regions. Conclusions Significant modifications of resting-state electrical brain activity were present after EMDR therapy. These findings suggest that the elaboration of psychological traumas induced by EMDR produces, in a resting-state condition, an enhancement of activity and functional connectivity of cerebral sources involved in cognitive control and emotional processing.

Resting-state functional connectivity of orthographic networks in acquired dysgraphia

Resting-state functional connectivity of orthographic networks in acquired dysgraphia

Utilizing rs-fMRI’s Mean to Localize Seizure Focus

Purpose: We present for the first time the utilization of an average of the resting-state time points (rs-Mean) to localize epilepsy focus in a group of pediatric patients with medical refractory epilepsy. Materials and Methods: Twenty nine pediatric patients had drug-resistant epilepsy, and 22 subject controls underwent rs-fMRI. Comparison among group means was performed. Regions of Interest (ROI) derived from the nuclear medicine studies (GS) and the focal hyperintensities yielded by the rs-Mean were converted into ellipsoids. Euclidian Distance (ED) among the isocenters of each ROI pair per patient was found. Matching of ROIs was accepted if the ED was equal or less than 50 mm. Group comparison was performed utilizing two-tail T-test. Statistical significance was accepted with p values < 0.01. Results: Statistically significant differences were found between the patient and normal control’s rs-Mean (p < 0.001, t (49) = 5.186; CI (95%) = [512.73, 1161.46]). The rs-Mean findings matched the GS findings in epilepsy patients in 72.4% of cases. The rs-Mean was positive matching GS in 6 of 9 (66.6%) MR-non-lesional cases. Conclusion: Our study demonstrates that rs-Mean in intractable epilepsy patients differs from normal subjects, and that it may demonstrate the epileptogenic focus in a significant number of patients.

Multimodal imaging brain connectivity analysis (MIBCA) toolbox: preliminary application to Alzheimer's disease.

The Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox is a fully automated all-in-one connectivity analysis toolbox that offers both pre-processing, connectivity, and graph theory analysis of multimodal images such as anatomical, diffusion, and functional MRI, and PET [1]. In this work, the MIBCA functionalities were used to study Alzheimer’s Disease in a multimodal MR/PET approach. Data from 11 healthy subjects and 10 AD patients were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu), including T1-weighted (T1w), Diffusion Tensor Imaging (DTI) data, and 18F-AV-45 (florbetapir) dynamic PET data from 40-60 min post injection (4x5min). Both MR and PET data were automatically pre-processed for all subjects using MIBCA. The T1w data was parcellated into cortical and subcortical regions-of-interest (ROIs), and the corresponding thicknesses and volumes were calculated. DTI data was used to compute structural connectivity matrices based on fibers connecting pairs of ROIs. Lastly, dynamic PET images were summed, and the Standard Uptake Values calculated for each ROI. An overall higher uptake of 18F-AV-45, consistent with an increased deposition of amyloid-Beta, was observed for the AD group. Additionally, patients showed significant cortical atrophy (thickness and volume) especially in the enthorhinal and temporal areas, and a significant increase in Mean Diffusivity (MD) in the hippocampus, amygdala and temporal areas, Figure ​Figure1.1. Furthermore, patients showed an overall decrease of both inter- and intra- hemispherical structural connections (tracts), Figure ​Figure2.2. Finally, the 3D-graph visualization showed that the structural loss was global and asymmetric, Figure 3. Figure 1 Group difference Connectrogram. Significant differences between Healthy Subjects and AD patients are highlighted in color in the inner rings. From outside to inside rings: region-of-interest (ROI), Standard Uptake Value (18F-AV-45), mean diffusivity, ... Figure 2 Group connectivity matrices for Healthy Subjects (upper row), and AD patients (lower row). From left to right are represented the structural, cortical thickness and cortical volume connectivity matrices. This work shows the potential of the MIBCA toolbox for the study of AD, as findings were shown to be in agreement with the literature [2–4]. Here, only structural changes and beta amyloid accumulation were considered. Yet, MIBCA is further able to process fMRI and different radiotracers, and combine all the information in order to provide new insights into AD.

EPA-1793 – Enhancement of right hemisphere eeg functional connectivity after emdr therapy

Introduction Brain connectivity changes have been recently demonstrated in victims of psychological traumas treated with the eye movement desensitization and reprocessing (EMDR). Objectives Forty victims of psychological traumas were investigated at the first EMDR session (t0) and at the last one performed after processing the index trauma (t1). Aims To investigate differences in EEG functional source connectivity during bilateral ocular stimulation (BS) during EMDR therapy at t0 and t1. Methods Brain electrical activity during whole EMDR sessions was record with a 37-channel EEG. EEG functional connectivity analysis was based on the lagged phase synchronization (LPS), derived by a two-step eLoreta procedure: dimensionality reduction of inverse matrix from 6239 voxels to 28 regions of interest (ROIs); LPS index computation, for each spectrum band, in all possible ROI pairs. Results Significant differences were detected between t0 and t1 in alpha band LPS indexes. A prevalent enhancement in right intrahemispheric functional connectivity was found in t1 respect to t0, particularly among ROI pairs of (a) frontal regions (anterior frontal, orbital frontal, lateral frontal cortices) and limbic structures (anterior cingulate cortex, ACC), (b) frontal regions and associative areas (insula cortex, parietal lobe), (c) ACC and primary visual cortex and (d) ACC and associative areas. Conclusions These findings suggest that EMDR efficacy is associated to electrical brain connectivity changes during BS. An enhancement in the right hemisphere alpha band functional connectivity of areas involved in cognitive control, emotional processing and visual associative functions may play a key role in the elaboration of psychological traumas.

Functional brain networks in REM sleep behavior disorder

REM sleep behavior disorder (RBD) has a strong association with synucleinopathies. Prior studies of psychometrics have demonstrated subtle abnormalities of task-switching and visuospatial tasks in iRBD, similar to deficits in synucleinopathies. In this study, we used resting state functional connectivity MRI to assess the strength of brain networks and regions involved in attention, executive control, visual, auditory, and motor function. Ten individuals (6 male, 4 female) with polysomnogram-confirmed iRBD were compared to ten age- and sex-matched controls. All underwent brain MRI including two blood-oxygen level dependent (BOLD) sequences acquired resting awake with eyes closed. Following standard pre-processing and regression of noise signals, BOLD time series were extracted from pre-defined regions of interest (ROI). The correlation coefficient between BOLD time series of each pair of ROIs was Fisher-z transformed to obtain normally-distributed data, prior to constructing cross-correlation matrices. Correlation coefficients for ROI-pairs were averaged to assess the connectivity strength of each network. Paired t-tests were used to compare iRBD cases and controls. There were no significant differences between iRBD cases and controls in the strength of the executive control, attention, or default mode networks. However, there was a significant decrease in connectivity between visual cortex and attention network (iRBD 0.06 vs. Control 0.14, p = 0.02). Additionally, the iRBD group had almost no correlation between the visual and primary motor regions, significantly lower than the robust correlation in controls (iRBD 0.08 vs. Control 0.33, p = 0.008). Resting state functional connectivity MRI analysis demonstrates significantly reduced strength of connections between the visual regions and the attention network, and between the visual and motor regions. The strength of canonical cognitive networks—including the default mode, attention, and executive networks—and connectivity of other sensory and motor regions, was not different between iRBD cases and controls. These findings suggest that the decline in visuo-spatial performance in iRBD reflects a specific disconnection of the visual regions from their usual functional networks, rather than a generalized brain dysfunction. This has implications for understanding how and where synucleinopathies affect the brain at the earliest stages of disease. American Sleep Medicine Foundation Physician Scientist Training Award.

Diagnosis of autism spectrum disorders using regional and interregional morphological features.

This article describes a novel approach to identify autism spectrum disorder (ASD) utilizing regional and interregional morphological patterns extracted from structural magnetic resonance images. Two types of features are extracted to characterize the morphological patterns: (1) Regional features, which includes the cortical thickness, volumes of cortical gray matter, and cortical-associated white matter regions, and several subcortical structures extracted from different regions-of-interest (ROIs); (2) Interregional features, which convey the morphological change pattern between pairs of ROIs. We demonstrate that the integration of regional and interregional features via multi-kernel learning technique can significantly improve the classification performance of ASD, compared with using either regional or interregional features alone. Specifically, the proposed framework achieves an accuracy of 96.27% and an area of 0.9952 under the receiver operating characteristic curve, indicating excellent diagnostic power and generalizability. The best performance is achieved when both feature types are weighted approximately equal, indicating complementary between these two feature types. Regions that contributed the most to classification are in line with those reported in the previous studies, particularly the subcortical structures that are highly associated with human emotional modulation and memory formation. The autistic brains demonstrate a significant rightward asymmetry pattern particularly in the auditory language areas. These findings are in agreement with the fact that ASD is a behavioral- and language-related neurodevelopmental disorder. By concurrent consideration of both regional and interregional features, the current work presents an effective means for better characterization of neurobiological underpinnings of ASD that facilitates its identification from typically developing children.

Su2106 Differences in Resting-State Bold Oscillation Signals Between Healthy and IBS Subjects

We hypothesized that alterations of this network can accompany the cognitive abnormalities observed in cirrhotic patients. The aim of the present study, therefore, was to determine and compare the resting functional connectivity of the DMN between healthy controls and cirrhotic patients with and without MHE. Methods: 30 cirrhotics (17 with MHE and 13 without MHE) [Table 1] and 16 healthy controls underwent resting-state functional magnetic resonance imaging (fMRI). Standard pre-processing was completed using Analysis of Functional NeuroImages (AFNI). Seed-based large-scale network was employed in the DMN and significant changes in connectivity were investigated. Correlation between activity in 14 apriori selected regions of interest (ROIs) of the DMN based on coordinates published in the literature were assessed. Unpaired t-test was used to differentiate the z-scored correlation coefficients between the ROI pairs and was corrected for multiple comparisons using false discovery rate (FDR). Results: Large-scale network analysis of the DMN showed significant differences in connectivity among controls and cirrhotics with MHE. Significant differences in connectivity were seen between the right posterior cingulate and the left posterior cingulate as well as between the right anterior cingulate and the right precuneus (p=0.004) [Figure 1]. Analysis also showed significant differences between cirrhotics with and without MHE. The significant differences included the right inferior parietal and the left anterior cingulate (p=0.006) [Figure 2]. There were no significant differences seen between controls and cirrhotics without MHE. Conclusion: Cirrhotic patients withminimal hepatic encephalopathy (MHE) exhibit significant alteration of the brain default-mode network when compared to cirrhotics without MHE and healthy controls Demographics

Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns

This article describes a novel approach to extract cortical morphological abnormality patterns from structural magnetic resonance imaging (MRI) data to improve the prediction accuracy of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Conventional approaches extract cortical morphological information, such as regional mean cortical thickness and regional cortical volumes, independently at different regions of interest (ROIs) without considering the relationship between these regions. Our approach involves constructing a similarity map where every element in the map represents the correlation of regional mean cortical thickness between a pair of ROIs. We will demonstrate in this article that this correlative morphological information gives significant improvement in classification performance when compared with ROI-based morphological information. Classification performance is further improved by integrating the correlative information with ROI-based information via multi-kernel support vector machines. This integrated framework achieves an accuracy of 92.35% for AD classification with an area of 0.9744 under the receiver operating characteristic (ROC) curve, and an accuracy of 83.75% for MCI classification with an area of 0.9233. In differentiating MCI subjects who converted to AD within 36 months from non-converters, an accuracy of 75.05% with an area of 0.8426 under ROC curve was achieved, indicating excellent diagnostic power and generalizability. The current work provides an alternative approach to extraction of high-order cortical information from structural MRI data for prediction of neurodegenerative diseases such as AD.

Characterization of short white matter fiber bundles in the central area from diffusion tensor MRI

Diffusion tensor imaging and tractography allow studying white matter fiber bundles in the human brain in vivo. Electrophysiological studies and postmortem dissections permit improving our knowledge about the short association fibers connecting the pre- and postcentral gyri. The aim of this study was first to extract and analyze the features of these short fiber bundles and secondly to analyze their asymmetry according to the subjects' handedness. Ten right-handed and ten left-handed healthy subjects were included. White matter fiber bundles were extracted using a streamline tractography approach, with two seed regions of interest (ROI) taken from a parcellation of the pre- and postcentral gyri. This parcellation was achieved using T1 magnetic resonance images (MRI) and semi-automatically generated three ROIs within each gyrus. MRI tracks were reconstructed between all pairs of ROIs connecting the adjacent pre- and postcentral gyri. A quantitative analysis was performed on the number of tracks connecting each ROI pair. A statistical analysis studied the repartition of these MRI tracks in the right and left hemispheres and as a function of the subjects' handedness. The quantitative analysis showed an increased density of MRI tracks in the middle part of the central area in each hemisphere of the 20 subjects. The statistical analysis showed significantly more MRI tracks for the left hemisphere, when we consider the whole population, and this difference was presumably driven by the left-handers. These results raise questions about the functional role of these MRI tracks and their relation with laterality.

P4‐258: Genome‐wide screen for variants that modify the known relationship between cerebrospinal fluid Beta‐Amyloid 42 and ptau levels

suggests that PIO improves or protects neuronal mitochondrial function and increases mitochondrial biogenesis in vivo and in vitro. Additionally, results of behavioral studies indicate that TZD treatment may ameliorate some of the cognitive deficits in murine models of Alzheimer’s disease (AD) and human AD. This initial exploration examined the effects of low-dose PIO treatment on brain functional connectivity in awake rats using fMRI. Methods: Awake, adult male Wistar rats were acclimated to the scanning procedure for approximately 7 days. A baseline scan was performed 2.53h after dosing with vehicle (citric acid) using a 4.7 Tesla/30cm horizontal magnet and blood-oxygen-level-dependent (BOLD) imaging. Thereafter, rats were dosed orally with vehicle or 1 of 4 dosages of PIO (0.04, 0.08, 0.16, 0.32 mg/kg). One group of vehicleand one group of PIO-treated rats were scanned after the 2nd dosing; all rats were scanned after the last (7th) daily dose (n1⁄45 for each group). Results: For region of interest (ROI) analysis, each brain was demarcated into 57 ROIs and cross-correlation coefficients between pairs of ROIs were calculated to evaluate functional connectivity between ROIs. Seventeen ROI pairs showed significant (P<0.005, uncorrected) changes in functional connectivity on Day 7 of treatment with PIO compared with vehicle. Significant changes were seen in two pairs of ROIs on day 2 of dosing with PIO compared to the control group. Using the CA1 region of the hippocampus as a seed region, a voxel-by-voxel analysis indicated increased connectivity between the seed and the hypothalamus and ventral thalamus after 7 days of dosing. Conclusions: Our results indicate that administration of pioglitazone at doses lower than those used to treat T2D leads to changes in functional connectivity in conscious rats, as assessed by fMRI BOLD imaging; some changesmay occur within 2 days of treatment. These results provide preliminary evidence that low doses of PIO lead to changes in the brain and support the hypothesis that this drug may be a beneficial pharmacotherapy for neurodegenerative diseases such as AD.

TU-A-218-05: Evaluation of Image Registration Using Landmark Matching and Texture Analysis

Purpose: To compare demons deformable registration with rigid and affineregistration methods by measuring registration accuracy and image texture changes introduced by each algorithm. Methods: Eleven temporally sequential pairs of clinical thoracic CT scans demonstrating no abnormal pathology were collected retrospectively from different patients. Rigid, affine, and demons registration methods were applied to all scan pairs using an appropriate choice of registration parameters. 150 landmarks were automatically selected in each baseline scan, and a semi‐automated approach was used to identify corresponding landmarks in the respective deformed follow‐up scans. Eleven first‐order texture features were calculated in 32x32‐pixel regions of interest (ROIs) randomly placed in the baseline scans and at anatomically matched locations in the demons‐, rigid‐, and affine‐ registered follow‐up scans. For each ROI pair, feature value differences between the three registered follow‐up scans and the baseline scan were calculated. Results: The average per‐patient Euclidean distance between baseline scan landmarks and registered follow‐up scan landmarks ranged from 0.01–0.50 mm (overall mean: 0.18 mm), 1.6–6.8 mm (overall mean: 3.95 mm), and 2.06–7.91 mm (overall mean: 4.82 mm) for demons, affine, and rigid registration methods, respectively. Across all eleven features, the variances in feature value change for rigid and affine registration methods were at least an order of magnitude larger than the corresponding feature‐ value‐change variances for demons registration. Conclusions: Demons registration achieved more accurate spatial alignment between temporally sequential CT scans than rigid or affine registration methods. Furthermore, the variance in texture feature value changes between baseline and registered follow‐up scan ROIs was smaller for demons than for rigid or affine registration methods. These advantages of demons registration observed with “normal” thoracic CT scans indicate that future studies may incorporate demons to combine serial CT scans such that automated methods may distinguish registration artifacts from actual pathologic change.

Predicting Regional Neurodegeneration from the Healthy Brain Functional Connectome

Neurodegenerative diseases target large-scale neural networks. Four competing mechanistic hypotheses have been proposed to explain network-based disease patterning: nodal stress, transneuronal spread, trophic failure, and shared vulnerability. Here, we used task-free fMRI to derive the healthy intrinsic connectivity patterns seeded by brain regions vulnerable to any of five distinct neurodegenerative diseases. These data enabled us to investigate how intrinsic connectivity in health predicts region-by-region vulnerability to disease. For each illness, specific regions emerged as critical network "epicenters" whose normal connectivity profiles most resembled the disease-associated atrophy pattern. Graph theoretical analyses in healthy subjects revealed that regions with higher total connectional flow and, more consistently, shorter functional paths to the epicenters, showed greater disease-related vulnerability. These findings best fit a transneuronal spread model of network-based vulnerability. Molecular pathological approaches may help clarify what makes each epicenter vulnerable to its targeting disease and how toxic protein species travel between networked brain structures.

P-793 - Reduced intracortical functional connectivity in huntington's disease

Functional network disruption in degenerative dementia has been reported. EEG coherence is used to assess functional connectivity between brain areas. Previous studies of Huntington's disease (HD) reported about electroencephalography (EEG) spectral power and source location, but coherence has not yet been examined. To examine EEG intracortical functional connectivity in HD using low-resolution brain electromagnetic tomography (LORETA). In 55 HD patients and 55 controls, 3-minute 19-channel vigilance-controlled EEG was recorded, and recomputed to current densities of 6239 cortical sLORETA voxels. These were recomputed into source model time series for 19 regions of interest (ROIs). Coherence overestimation due to volume conduction was avoided by computing functional connectivity as ‘lagged’ coherence. This was done for each ROI pair (19*18/2=171) in each of 8 EEG frequency bands (delta through gamma). Statistics tested coherences (a) HD patients versus controls, and (b) HD patients in early versus late disease stages. (a) HD patients showed only reduced connectivities compared to controls (p < 0.05 corrected for multiple comparison), involving EEG theta, alpha-1-2 and beta1-2-3 frequency bands. The largest number of reduced connectivities occurred in alpha-1 (79 cases) and beta-2 (96 cases). (b) HD stage-1 versus stage-3-4 revealed only one significant difference. HD compared to controls showed massive reduction of functional connectivity. This occurred early and remained stable during disease progression. As in other dementing disorders, for example Alzheimer disease, the largest reduction concerned alpha and beta EEG frequencies. The results suggest a neocortical disconnection syndrome of a primarily subcortical disease.

Hierarchical anatomical brain networks for MCI prediction: revisiting volumetric measures.

Owning to its clinical accessibility, T1-weighted MRI (Magnetic Resonance Imaging) has been extensively studied in the past decades for prediction of Alzheimer's disease (AD) and mild cognitive impairment (MCI). The volumes of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) are the most commonly used measurements, resulting in many successful applications. It has been widely observed that disease-induced structural changes may not occur at isolated spots, but in several inter-related regions. Therefore, for better characterization of brain pathology, we propose in this paper a means to extract inter-regional correlation based features from local volumetric measurements. Specifically, our approach involves constructing an anatomical brain network for each subject, with each node representing a Region of Interest (ROI) and each edge representing Pearson correlation of tissue volumetric measurements between ROI pairs. As second order volumetric measurements, network features are more descriptive but also more sensitive to noise. To overcome this limitation, a hierarchy of ROIs is used to suppress noise at different scales. Pairwise interactions are considered not only for ROIs with the same scale in the same layer of the hierarchy, but also for ROIs across different scales in different layers. To address the high dimensionality problem resulting from the large number of network features, a supervised dimensionality reduction method is further employed to embed a selected subset of features into a low dimensional feature space, while at the same time preserving discriminative information. We demonstrate with experimental results the efficacy of this embedding strategy in comparison with some other commonly used approaches. In addition, although the proposed method can be easily generalized to incorporate other metrics of regional similarities, the benefits of using Pearson correlation in our application are reinforced by the experimental results. Without requiring new sources of information, our proposed approach improves the accuracy of MCI prediction from (of conventional volumetric features) to (of hierarchical network features), evaluated using data sets randomly drawn from the ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset.